1. Field of the Invention
The present invention generally relates to a method for correcting an object image for identification. More particularly, the invention relates to the method for performing shifting, rotation, scaling, or/and direction corrections on a specific object image before image identification, in order to reduce time consumption for identification.
2. Description of Related Art
Facial identification in the application of safety management becomes more sophisticated. In the conventional way of identification, a database of digitized facial features is usually established at first step. In practical, the face image of a user is captured through a camera lens, and the facial features, such as eyes, nose, and mouth, are retrieved. The retrieved features are then compared with the features registered in the database for determining the privilege of specific resource.
In the procedure of basic comparison, it is difficult to identify the face since the scale of the inputted facial frame is different from the image registered in the database, or the frame deviates left or right. For solving the mentioned problem, an instant scaling or shifting correction of the face image is necessary for the identification. After that, an accurate result can be obtained in comparison with the data in database.
However, the process of identification needs to spend a lot of time and repeat the comparison procedure if the image does not undergo the correction of scaling or shifting. For example, if the time spent for once comparison is t, the time may be number or hundreds of times as processing scaling and shifting adjustment.
The facial identification technology in the prior art may be referred FIG. 1, which illustrates a method for positioning a facial feature in an image. A background image of an inputted image is eliminated, and the direction of face is corrected in a pre-process. The corrected image is then compared with a reference image marked with a human's face. The method separately calculates the changes of the pixel columns and the pixels of the reference image and the inputted image. The corresponding relation there-between may be obtained. Therefore, an image with marked facial feature may be retrieved.
In the beginning of the steps, an inputted image is retrieved in step S101. Since the inputted image covers the whole picture, the background colors may interfere with the process of face identification except for the human face. Thus, the method is to compute the color difference, and to find out the distribution of the facial features through the edge of color palette. For example, the skin color difference may be used to find out the face region of the inputted image (step S103). In the example, a mean-shift algorithm is used to process a color segment on the inputted image based on a skin color model. After analysis and comparison, the background noise can be filtered out, so as to obtain the facial region near the skin color in the image.
Next, the method goes to identify the position of the facial feature within the facial region, such as the position of eyes, nose, or mouth. That is to find the feature blocks based on the differences of skin color on each region (step S105).
Because the facial region may be slanted, the face should be aligned to correct the direction before the process of comparison. Such as step S107, it's to rotate the facial region according to the level of a tilt angle of the facial feature. After that, the corrected image is compared with the reference image marked with the facial feature, and the correlation there-between is found (step S109).
It is noted that the recognition can be enhanced and makes time reduction when the image is adjusted based on the registered image in the database.